Assessing Groundwater Vulnerability to Contamination in a Semi-Arid Environment Using DRASTIC and GOD Models, Case of F’kirina Plain, North of Algeria

International audienceGroundwater is vulnerable to contamination by anthropological activities. Vulnerability mapping isconsidered as a fundamental aspect of groundwater management. The aim of this study was to estimate aquifervulnerability by applying the DRASTIC and GOD models in F’kirina agricultural plain northern Algeria. TheDRASTIC model uses seven environmental parameters (depth to water, net recharge, aquifer media, soil media,topography, impact of vadose zone, and hydraulic conductivity) to characterize the hydrogeological setting andevaluate aquifer vulnerability. GOD is an overlay and index method designed to map groundwater vulnerabilityover large regions based on three parameters (groundwater confinement, overlying strata, and depth togroundwater). The information layers for models were provided via geographic information system. The resultsshowed that the DRASTIC model is better than GOD model to estimate groundwater vulnerability to pollutionin the measured wells. For DRASTIC model, the correlation coefficient between vulnerability index and nitrateconcentration was 68 % that was substantially higher than 28 % obtained for the GOD model. We can concludethat nitrate concentration should be a suitable parameter to investigate the accuracy of the DRASTIC and GODmodels

Hydrochemical characterisation of groundwater using multifactorial approach in Foum el Gueiss basin, Northeastern Algeria

Knowledge of the quantity and quality of groundwater is a prerequisite to encourage investment in the development of a region and to consider the sedentarisation of populations. This work synthesises and analyses data concerning the chemical quality of the available water acquired in the Foum el Gueiss catchment area in the Aures massif. Two families of waters are observed, on the one hand, calcium and magnesian chlorated-sulphate waters and on the other hand, calcium and magnesium bicarbonate waters. Multivariate statistical treatments (Principal Component Analysis – PCA and Discriminant Analysis – DA) highlight a gradient of minerality of the waters from upstream to downstream, mainly attributed to the impact of climate, and pollution of agricultural origin rather localised in the lower zones. These differences in chemical composition make it possible to differentiate spring, well and borehole waters. The main confusion is between wells and boreholes, which is understandable because they are adjacent groundwater, rather in the lower part of the catchment area. The confusion matrix on the dataset shows a complete discrimination with a 100% success rate. There is a real difference between spring water and other samples, while the difference between wells and boreholes is smaller. The confusion matrix for the cross-validation (50%)